AI program baseline assessment
Establish an honest baseline for an AI program
AI program baseline assessment becomes relevant when an AI rollout is approaching without a reliable picture of current workflow performance. A focused review would examine a prechange workflow population measured over a declared period and case mix. This is most relevant to program sponsors and finance partners, especially where teams must distinguish historical record and retrospective estimate.
A measure needs a unit, comparator and decision
Illustrative working format
- Measurement unit
- A prechange workflow population measured over a declared period and case mix.
- Historical workflow records
- Establish the starting evidence and its measurement boundary.
- Retrospective estimate
- Keep this distinct when interpreting the proposed measure.
- A prospective measurement and comparator plan
- Connect uncertainty to the management choice it can change.
Decision at stake
Establish an honest baseline for an AI program
Make the decision explicit
- Program sponsors and finance partners
- An AI rollout is approaching without a reliable picture of current workflow performance.
- Decision
- What the program can credibly compare against and which missing data must be collected before rollout.
- Historical record
- Use historical workflow records to establish the relevant starting condition.
For program sponsors and finance partners, the immediate question is what the program can credibly compare against and which missing data must be collected before rollout. This is most useful when there is a real choice between proceeding, changing the design, collecting more evidence or narrowing the intended use. The starting conversation should identify that choice and its consequence before deciding how much work the topic warrants.
A baseline assembled after rollout could reflect changed demand or selective recollection rather than the process the agent replaced. A useful review follows a prechange workflow population measured over a declared period and case mix, rather than stopping at a convenient component metric or document boundary. The evidence should separate observation, interpretation and recommendation so the accountable owner can see what it supports.
Prerequisites
Start with historical workflow records
Give each input a specific job
- Historical workflow records
- Defines the operating boundary.
- Human effort and exception samples
- Examine typical case.
- Quality, demand and seasonality context
- Shows conditions affecting stable baseline.
- Business and finance owner
- Confirm access and a reviewer who can resolve gaps.
For AI program baseline assessment, the sponsor should identify the decision, authoritative data sources and the owner of each measure. A baseline or credible comparator is especially important. Where the relevant information is missing, the work should establish a prospective measurement plan or narrow the claim rather than manufacture historical precision.
Useful starting material would include historical workflow records; human effort and exception samples; quality, demand and seasonality context. These inputs have different jobs: the first establishes context, the second supplies behaviour or comparison material, and the third exposes operating conditions that may qualify a conclusion. Existing material would be reviewed before requesting new collection, and a missing input would be recorded as an evidence gap rather than treated as a favourable result.
Coverage
Historical record and retrospective estimate need different evidence
Keep the review distinctions visible
- Historical record
- Report separately from retrospective estimate.
- Typical case
- Report separately from eligible population.
- Stable baseline
- Report separately from changing operating conditions.
Historical record versus retrospective estimate
Baseline review would examine whether historical records capture accepted completion or merely ticket closure. Reopened cases and missing work can materially change the process description even when aggregate volumes reconcile.
Typical case versus eligible population
Effort collection would cover preparation, checking and exceptions across relevant task groups. A sample of convenient routine cases should not become the reference for a rollout intended to handle difficult work.
Stable baseline versus changing operating conditions
The comparator record would describe seasonality, staffing and process changes. If the prechange population cannot be reconstructed credibly, a prospective observation plan would replace a retrospective savings claim.
Illustrative scenario
Aggregate ticket history: examine what changes the decision
Examine the constructed decision boundary
Illustrative scenario
- Aggregate ticket history
A purchase-preparation team lacks task and review detail before automation.
- Baseline check
Sample case types, reopened requests and human effort.
- Prospective option
Collect a new baseline if the historical record cannot support comparison.
In an illustrative case, a team plans to automate purchase-request preparation but has only aggregate ticket counts. The proposed baseline would sample task types, examine reopened requests and record preparation and review effort. If historical data cannot support a credible comparison, the recommendation would be prospective observation instead of manufacturing a precise savings figure.
The prospective sample might reveal that reopened requests consume more effort than initial preparation. That would change the baseline question and potentially the automation hypothesis. The record would preserve the original assumption and explain the revised collection plan, avoiding a convenient baseline that excludes the difficult part of the work. These are hypothetical decision paths, not observed client outcomes.
How the work proceeds
Define the comparison before interpreting the value
Follow the evidence through the work
- Historical workflow records
Define the unit, period and comparator before interpreting a change.
- Human effort and exception samples
Inspect this evidence for missing cases and incompatible definitions.
- Typical case
Keep this distinction visible when assessing the apparent value.
- A prospective measurement and comparator plan
Show assumptions and the observation that could reverse the decision.
Define the measurement
Define the eligible workflow population and prechange observation boundary. The opening step turns the operating concern into a question that can be examined. It would identify a prechange workflow population measured over a declared period and case mix, the outcome the owner cares about and the conditions that would make an observation decision-relevant. Scope changes would remain explicit if the evidence reveals a different problem.
Check the evidence
Assess source completeness, case mix and measurement consistency. The comparison material would include human effort and exception samples, with quality, demand and seasonality context available to explain the operating conditions. Differences between historical record and retrospective estimate would remain visible beside the observation rather than disappear into a pooled result.
Examine alternatives
Collect missing effort and quality evidence through proportionate sampling. The review question would explicitly distinguish typical case from eligible population. A material discrepancy would be traced back to historical workflow records and the relevant execution or decision record, so a proposed change addresses the observed cause rather than the most convenient explanation.
Support the decision
Document a baseline with uncertainty, exclusions and refresh conditions. The resulting prospective measurement and comparator plan would explain the distinction between stable baseline and changing operating conditions and identify what further observation would change the recommendation. The business and finance sponsor would retain the decision, including whether a narrower scope or a different approach is preferable.
Indicative artefacts
Use a prospective measurement and comparator plan to make the next action explicit
Every artefact needs an operating use
Illustrative working format
- A baseline data-quality assessment
- Boundary — historical workflow records.
- A workflow performance reference
- Findings about typical case.
- A prospective measurement and comparator plan
- Action on stable baseline.
The final artefact set depends on the agreed decision. Useful outputs can include:
- A baseline data-quality assessment. The working foundation for the question, including relevant definitions, boundaries and assumptions so another reviewer can understand what was examined.
- A workflow performance reference. The substantive observations and interpretation, preserving adverse results, disagreement and limitations that affect how the evidence may be used.
- A prospective measurement and comparator plan. The connection to action: what the owner could do next, what evidence is still missing and what change would justify revisiting the conclusion.
Scope and authority
Measured evidence and assumptions have different roles in a funding decision
Keep analytical work separate from decision authority
- Defined measures and management analysis
- Can include a baseline data-quality assessment.
- Business and finance owner
- Finance and business owners validate assumptions and retain funding decisions.
- Audited returns or investment approval
- Cannot be inferred from a workflow performance reference.
The proposed work supports management analysis and does not approve an investment or establish audited financial results. Prices, returns and realized benefits depend on actual evidence. Accounting, regulated advice and legal interpretations remain with qualified owners; authoritative financial and CRM records remain in their approved systems. To discuss ai program baseline assessment, the first practical step is to identify the relevant owner and bring a sanitised description of the decision, the current alternative and the material uncertainty. That gives scoping a concrete starting point without implying a preapproved package or favourable conclusion.
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